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In April 1233 Gregory IX issued bulls 
authoring the establishment of inquisitorial tribunals in Languedoc. By January 1234 the
provincial prior of the Dominicans in Toulouse was able to present a papal legate with a
list of inquisitors.
One of the inquisitors’ first victims fell into their hands on the very
day when the canonization of St. Dominic was proclaimed in the city of Toulouse. On
August 5, 1234, the bishop of Toulouse, Raymond of Miramont, said a solemn mass in
Dominic’s honor in the Dominicans’ residence. As he and the friars were entering the 
convent’s refectory, “through the merits of the Blessed Dominic,” as Pelhisson put it, a
man told the convent’s rector that some Cathar heretics were in the process of
administering to a dying believer the consolamentum, the ritual which enabled an
individual to escape from the demon-created prison of this world back to his/her true
home in heaven. This was happening at the house of Peitavin Boursier, who Pelhisson
claimed had long been “something of a general courier” for the heretics.
Seating himself by her bedside, Bishop Raymond launched into a long discussion
about contempt for the world. Boursier’s mother-in-law, who had just received the
consolamentum, thought she was talking to one of the Cathar Good Christians. The
bishop was able to get her to admit to many heretical beliefs. He then said, “For the rest,
you must not lie nor have much concern for this miserable life…Hence, I say that you are
to be steadfast in your belief, nor in fear of death ought you to confess anything other
than what you believe and hold firmly to your heart.” The dying woman answered, “My
lord, what I say I believe, and I shall not change my commitment out of concern for the
miserable remnant of my life.” The bishop replied, “Therefore you are a heretic! For
what you have confessed is the faith of the heretics, and you may know assuredly that the
The prior
informed the bishop, and a crowd went to Boursier’s house. There they found Boursier’s
mother-in-law suffering from a high fever. One of those gathered at her sick-bed called
out, “Look, my lady, the lord bishop is coming to see you.” But the bishop and the others
entered the house so quickly that he did not have an opportunity to tell her that her visitor
was the Catholic bishop of Toulouse, not a Cathar bishop.
heresies are manifest and condemned. Renounce them all! Accept what the Roman and 
catholic church believes. For I am your bishop of Toulouse, and I preach the Roman
Catholic faith, which I want and urge you to believe.” Boursier’s mother-in-law
courageously proved true to her vow, and refused to recant. The bishop condemned her.
She was immediately picked up, bed and all, and taken out of the city and burned.
As she cooked in a meadow belonging to the count of Toulouse, the bishop and the friars
happily repaired to their dinner.

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Brainy music for brainy people


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Andrew Grassie - "Sculpting Time"





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Esta canção dá-me vontade de gritar



"There once was a poodle who thought he was a cowboy, but he lived in a cage the size of his thumb.

And, though his white horse was a box of toothpicks, he galloped around until hit by a car.


Sometimes I flap my arms like a hummingbird just to remind myself I'll never fly.

Sometimes I burn my arms with cigarettes just to pretend I won't scream when I die. 

Sometimes I can't wait to come down with cancer. At least then I'll get to watch tv all day.

And on my deathbed I'll get all the answers even if all my questions are taken away.

If my life was as long as the moon's, I'd still be jealous of the sun.

If my life lasted only one day, I'd still be drunk by noon."

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Thomas Davis, "The West’s Asleep"



When all beside a vigil keep,

The West’s asleep, the West’s asleep-
Alas! and well may Erin weep,
When Connaught lies in slumber deep.
There lake and plain smile fair and free,
‘Mid rocks-their guardian chivalry-
Sing oh! let man learn liberty
From crashing wind and lashing sea.

That chainless wave and lovely, land
Freedom and Nationhood demand-
Be sure, the great God never plann’d,
For slumbering slaves, a home so grand.
And, long, a brave and haughty race
Honoured and sentinelled the place-
Sing oh! not even their sons’ disgrace
Can quite destroy their glory’s trace.

For often, in O’Connor’s van,
To triumph dash’d each Connaught clan-
And fleet as deer the Normans ran
Through Coirrsliabh Pass and Ard Rathain.*
And later times saw deeds as brave;
And glory guards Clanricarde’s grave-
Sing oh! they died their land to save,
At Aughrim’s slopes and Shannon’s wave.

And if, when all a vigil keep,
The West’s; asleep, the West’s asleep-
Alas! and well may Erin weep,
That Connaught lies in slumber deep.
But-hark! -some voice like thunder spake:
” The West’s awake, the West’s awake’-
Sing oh! hurra! let England quake,
We’ll watch till death for Erins sake!”


Via Manifesto Conservador


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"I sing the body electric"


"Human beings can, and still do, send orders from their computers to the matching engines, but this accounts for less than half of all US share trading. The remainder is algorithmic: it results from share-trading computer programs. Some of these programs are used by big institutions such as mutual funds, pension funds and insurance companies, or by brokers acting on their behalf. The drawback of being big is that when you try to buy or sell a large block of shares, the order typically can’t be executed straightaway (if it’s a large order to buy, for example, it will usually exceed the number of sell orders in the matching engine that are close to the current market price), and if traders spot a large order that has been only partly executed they will change their own orders and their price quotes in order to exploit the knowledge. The result is what market participants call ‘slippage’: prices rise as you try to buy, and fall as you try to sell.

In an attempt to get around this problem, big institutions often use ‘execution algorithms’, which take large orders, break them up into smaller slices, and choose the size of those slices and the times at which they send them to the market in such a way as to minimise slippage. For example, ‘volume participation’ algorithms calculate the number of a company’s shares bought and sold in a given period – the previous minute, say – and then send in a slice of the institution’s overall order whose size is proportional to that number, the rationale being that there will be less slippage when markets are busy than when they are quiet. The most common execution algorithm, known as a volume-weighted average price or VWAP algorithm (it’s pronounced ‘veewap’), does its slicing in a slightly different way, using statistical data on the volumes of shares that have traded in the equivalent time periods on previous days. The clock-time periodicities found by Hasbrouck and Saar almost certainly result from the way VWAPs and other execution algorithms chop up time into intervals of fixed length.


‘Electronic market-making’ algorithms replicate what human market makers have always tried to do – continuously post a price at which they will sell a corporation’s shares and a lower price at which they will buy them, in the hope of earning the ‘spread’ between the two prices – but they revise prices as market conditions change far faster than any human being can. Their doing so is almost certainly the main component of the flood of orders and cancellations that follows even minor changes in supply and demand.

‘Statistical arbitrage’ algorithms search for transient disturbances in price patterns from which to profit. For example, the price of a corporation’s shares often seems to fluctuate around a relatively slow-moving average. A big order to buy will cause a short-term increase in price, and a sell order will lead to a temporary fall. Some statistical arbitrage algorithms simply calculate a moving average price; they buy if prices are more than a certain amount below it and sell if they are above it, thus betting on prices reverting to the average. More complicated algorithms search for disturbances in price patterns involving more than one company’s shares.


No one in the markets contests the legitimacy of electronic market making or statistical arbitrage. Far more controversial are algorithms that effectively prey on other algorithms. Some algorithms, for example, can detect the electronic signature of a big VWAP, a process called ‘algo-sniffing’. This can earn its owner substantial sums: if the VWAP is programmed to buy a particular corporation’s shares, the algo-sniffing program will buy those shares faster than the VWAP, then sell them to it at a profit. Algo-sniffing often makes users of VWAPs and other execution algorithms furious: they condemn it as unfair, and there is a growing business in adding ‘anti-gaming’ features to execution algorithms to make it harder to detect and exploit them. However, a New York broker I spoke to last October defended algo-sniffing:

I don’t look at it as in any way evil … I don’t think the guy who’s trying to hide the supply-demand imbalance [by using an execution algorithm] is any better a human being than the person trying to discover the true supply-demand. I don’t know why … someone who runs an algo-sniffing strategy is bad … he’s trying to discover the guy who has a million shares [to sell] and the price then should readjust to the fact that there’s a million shares to buy.

Whatever view one takes on its ethics, algo-sniffing is indisputably legal. More dubious in that respect is a set of strategies that seek deliberately to fool other algorithms. An example is ‘layering’ or ‘spoofing’. A spoofer might, for instance, buy a block of shares and then issue a large number of buy orders for the same shares at prices just fractions below the current market price. Other algorithms and human traders would then see far more orders to buy the shares in question than orders to sell them, and be likely to conclude that their price was going to rise. 


Speeds are increasing all the time. In Hasbrouck and Saar’s data, which come from 2007 and 2008, the salient unit of trading time was still the millisecond, but that’s now beginning to seem almost leisurely: time is often now measured in microseconds (millionths of a second). The London Stock Exchange, for example, says that its Turquoise trading platform can now process an order in as little as 124 microseconds. Some market participants are already talking in terms of nanoseconds (billionths of a second), though that’s currently more marketing hype than technological reality.

Because the timescales of trading have changed, the significance of space has also altered. A few years ago, it was common to proclaim the ‘end of geography’ in financial markets, and it’s certainly true that if one is thinking in terms of hour-by-hour or even minute-by-minute market movements, it doesn’t really matter whether a trader is based in London, New York, Tokyo, Singapore or São Paulo. However, that’s not the case in high-frequency trading. Imagine, for example, that your office is in Chicago, the second largest financial centre in the US, and you want to trade on the New York Stock Exchange. You are around 800 miles away from the matching engines in Mahwah, and sending a message that distance, using the fastest fibre-optic route between Chicago and New Jersey that I know of, takes around 16 milliseconds. That’s a huge delay: you might as well be on the moon.


The solution is what’s called ‘colocation’: placing the computer systems on which your algorithms run next to the matching engines in data centres such as Mahwah. Colocation isn’t cheap – a single rack on which to place your server can cost you $10,000 a month, and it has become a big earner for exchanges and other electronic trading venues – but it’s utterly essential to high-frequency trading. Even the precise whereabouts of your computers within data centres is a matter of some sensitivity: you hear tales (possibly apocryphal) of traders gaining entry to centres and trying to have holes drilled in walls so that the route from their server to the matching engine is shorter


The overall prices of US shares, and of the index futures contracts that are bets on those prices, fell by about 6 per cent in around five minutes, a fall of almost unprecedented rapidity (it’s typical for broad market indices to change by a maximum of between 1 and 2 per cent in an entire day). Overall prices then recovered almost as quickly, but gigantic price fluctuations took place in some individual shares. Shares in the global consultancy Accenture, for example, had been trading at around $40.50, but dropped to a single cent. Sotheby’s, which had been trading at around $34, suddenly jumped to $99,999.99. The market was already nervous that day because of the Eurozone debt crisis (in particular the dire situation of Greece), but no ‘new news’ arrived during the critical 20 minutes that could account for the huge sudden drop and recovery, and nothing had been learned about Accenture to explain its shares losing almost all their value.


The trigger was indeed an algorithm, but not one of the sophisticated ultra-fast high-frequency trading programs. It was a simple ‘volume participation’ algorithm, and while the official investigation does not name the firm that deployed it, market participants seem convinced that it was the Kansas City investment managers Waddell & Reed. The firm’s goal was to protect the value of a large position in the stock market against further declines, and it did this by programming the algorithm to sell 75,000 index future contracts. (These contracts track the S&P 500 stock-market index, and each contract was equivalent to shares worth a total of around $55,000. The seller of index futures makes money if the underlying index falls; the buyer gains if it rises.) The volume participation algorithm calculated the number of index futures contracts that had been traded over the previous minute, sold 9 per cent of that volume, and kept going until the full 75,000 had been sold. The total sell order, worth around $4.1 billion, was unusually large, though not unprecedented: the SEC/CFTC investigators found two efforts in the previous year to sell the same or larger quantities of futures in a single day. But the pace of the sales on 6 May was very fast.

On both those previous occasions, the market had been able to absorb the sales without crashing. In the first few minutes after the volume participation algorithm was launched, at 2.32 p.m. on 6 May, it looked as if the market would be able to do so again. Electronic market-making algorithms bought the futures that the volume participation algorithm was selling, as did index-arbitrage algorithms. (These programs exploit discrepancies between the price of index futures and the price of the underlying shares. A large sell order in the index futures market will often create just such a discrepancy, which can be profited from by buying index futures and selling the underlying shares.) Algorithmic trading was still in the benign zone that it occupies most of the time: electronic market makers and arbitrageurs were ‘providing liquidity’, as market participants put it, making it possible for the volume participation algorithm to do its intended large-scale selling.

However, high-frequency traders usually program their algorithms to be ‘market neutral’, in other words to insulate their trading positions from fluctuations in overall market levels. From around 2.41 p.m., therefore, those algorithms started to sell index futures to counterbalance their purchases, and the electronic index futures market entered a spasm of the kind identified by Hasbrouck and Saar. One algorithm would sell futures to another algorithm, which in its turn would try to sell them again, in a pattern that the SEC/CFTC investigators call ‘hot potato’ trading. In the 14-second period following 2.45 and 13 seconds, more than 27,000 futures contracts were bought and sold by high-frequency algorithms, but their aggregate net purchases amounted to only around 200 contracts. By 2.45 and 27 seconds, the price of index futures had declined by more than 5 per cent from its level four and a half minutes earlier. The market had entered a potentially catastrophic self-feeding downward spiral.

Fortunately, though, the electronic trading platform on which these index futures were being bought and sold – the Chicago Mercantile Exchange’s Globex system – is programmed to detect just such a spiral. Its ‘Stop Logic Functionality’ is designed to interrupt self-feeding crashes and upward price spikes. A ‘stop’ is an order that is triggered automatically when prices reach a preset adverse level. Buyers of index futures, for example, will sometimes try to protect themselves from catastrophic losses by placing stop orders that will sell those futures if their prices fall below a given level. However, these sales can potentially begin a cascade, causing further price falls which in turn trigger further stop orders. The goal of the Stop Logic Functionality is to halt this process by giving human traders time to assess what is happening, step in and pick up bargains.


Pushing the red button on an official market maker’s system, therefore, did not entirely remove the bids to buy and offers to sell, but reduced the bids to the lowest possible price that could be entered into electronic trading systems (one cent), and increased the offers to the maximum possible price ($99,999.99). These ‘stub quotes’ allow market makers to fulfil their formal obligations, while being so hopelessly unattractive that under normal circumstances no one would ever want to take a market maker up on them. In the case of several stocks, however, the evaporation of the market by around 2.45 p.m. was so complete that stub quotes were the only ones left. In consequence, ‘market orders’ (orders simply to buy or to sell at the best available price) were executed against stub quotes, hence Accenture’s price of a cent and Sotheby’s of $99,999.99.


As Steve Wunsch, one of the pioneers of electronic exchanges, put it in another TABB forum discussion, US share trading ‘is now so complex as a system that no one can predict what will happen when something new is added to it, no matter how much vetting is done.’ If Wunsch is correct, there is a risk that attempts to make the system safer – by trying to find mechanisms that would prevent a repetition of last May’s events, for example – may have unforeseen and unintended consequences.

Systems that are both tightly coupled and highly complex, Perrow argues in Normal Accidents (1984), are inherently dangerous. Crudely put, high complexity in a system means that if something goes wrong it takes time to work out what has happened and to act appropriately. Tight coupling means that one doesn’t have that time. Moreover, he suggests, a tightly coupled system needs centralised management, but a highly complex system can’t be managed effectively in a centralised way because we simply don’t understand it well enough; therefore its organisation must be decentralised. Systems that combine tight coupling with high complexity are an organisational contradiction, Perrow argues: they are ‘a kind of Pushmepullyou out of the Doctor Dolittle stories (a beast with heads at both ends that wanted to go in both directions at once)’.

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Don´t fuck with da Man


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This song is festering in me




I wish that I had known in 
That first minute we met
The unpayable debt 
That I owed you

Because you'd been abused 
By the bone that refused you
And you hired me 
To make up for that

And walking in that room 
When you had tubes in your arms
Those singing morphine alarms 
Out of tune

They had you sleeping and eating 
And I didn't believe them
When they called you 
A hurricane thundercloud

When I was checking vitals
I suggested a smile
You didn't talk for a while 
You were freezing

You said you hated my tone
It made you feel so alone
So you told me 
I had to be leaving

But something kept me standing 
By that hospital bed
I should have quit but instead
I took care of you

You made me sleep all uneven
And I didn't believe them
When they told me that there
Was no saving you 

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"Computer Scientists Induce Schizophrenia in a Neural Network, Causing it to Make Ridiculous Claims"



AUSTIN, Texas — Computer networks that can’t forget fast enough can show symptoms of a kind of virtual schizophrenia, giving researchers further clues to the inner workings of schizophrenic brains, researchers at The University of Texas at Austin and Yale University have found.

The researchers used a virtual computer model, or “neural network,” to simulate the excessive release of dopamine in the brain. They found that the network recalled memories in a distinctly schizophrenic-like fashion.

Their results were published in April in Biological Psychiatry.

“The hypothesis is that dopamine encodes the importance — the salience — of experience,” says Uli Grasemann, a graduate student in the Department of Computer Science at The University of Texas at Austin. “When there’s too much dopamine, it leads to exaggerated salience, and the brain ends up learning from things that it shouldn’t be learning from.”

The results bolster a hypothesis known in schizophrenia circles as the hyperlearning hypothesis, which posits that people suffering from schizophrenia have brains that lose the ability to forget or ignore as much as they normally would. Without forgetting, they lose the ability to extract what’s meaningful out of the immensity of stimuli the brain encounters. They start making connections that aren’t real, or drowning in a sea of so many connections they lose the ability to stitch together any kind of coherent story.

The neural network used by Grasemann and his adviser, Professor Risto Miikkulainen, is called DISCERN. Designed by Miikkulainen, DISCERN is able to learn natural language. In this study it was used to simulate what happens to language as the result of eight different types of neurological dysfunction. The results of the simulations were compared by Ralph Hoffman, professor of psychiatry at the Yale School of Medicine, to what he saw when studying human schizophrenics.

In order to model the process, Grasemann and Miikkulainen began by teaching a series of simple stories to DISCERN. The stories were assimilated into DISCERN’s memory in much the way the human brain stores information-not as distinct units, but as statistical relationships of words, sentences, scripts and stories.

“With neural networks, you basically train them by showing them examples, over and over and over again,” says Grasemann. “Every time you show it an example, you say, if this is the input, then this should be your output, and if this is the input, then that should be your output. You do it again and again thousands of times, and every time it adjusts a little bit more towards doing what you want. In the end, if you do it enough, the network has learned.”

In order to model hyperlearning, Grasemann and Miikkulainen ran the system through its paces again, but with one key parameter altered. They simulated an excessive release of dopamine by increasing the system’s learning rate-essentially telling it to stop forgetting so much.

“It’s an important mechanism to be able to ignore things,” says Grasemann. “What we found is that if you crank up the learning rate in DISCERN high enough, it produces language abnormalities that suggest schizophrenia.”

After being re-trained with the elevated learning rate, DISCERN began putting itself at the center of fantastical, delusional stories that incorporated elements from other stories it had been told to recall. In one answer, for instance, DISCERN claimed responsibility for a terrorist bombing.

In another instance, DISCERN began showing evidence of “derailment”-replying to requests for a specific memory with a jumble of dissociated sentences, abrupt digressions and constant leaps from the first- to the third-person and back again.

“Information processing in neural networks tends to be like information processing in the human brain in many ways,” says Grasemann. “So the hope was that it would also break down in similar ways. And it did.”

The parallel between their modified neural network and human schizophrenia isn’t absolute proof the hyperlearning hypothesis is correct, says Grasemann. It is, however, support for the hypothesis, and also evidence of how useful neural networks can be in understanding the human brain.

“We have so much more control over neural networks than we could ever have over human subjects,” he says. “The hope is that this kind of modeling will help clinical research.”

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